AI coding tools make individual developers faster — and we think you should keep yours. OnTrack AI runs the loop around them, so what your business asked for is what provably ships — inside the Azure DevOps or Jira you already run.
Complement, not competitor
| Outcome | OnTrack AI | Claude Codecoding agent | Copilotcoding assistant | Kirospec-driven IDE | Devinautonomous coder |
|---|---|---|---|---|---|
| Before the code — a governed backlog | |||||
| Your written requirements arrive as a ready backlog | ✕ | ✕ | Partial1 | ✕ | |
| Nothing reaches your board without human sign-off | ✕ | ✕ | ✕ | ✕ | |
| After the code — proof, not promises | |||||
| Every story arrives with its tests already written | ✕ | ✕ | ✕ | ✕ | |
| Every release ships with its own proof | ✕ | ✕ | ✕ | ✕ | |
| “Prove it works” is answered with a link, not a fire drill | ✕ | ✕ | ✕ | ✕ | |
| Around the AI — enterprise governance | |||||
| Your standards apply to every agent, automatically | Partial2 | Partial2 | Partial2 | ✕ | |
| Your AI vendor choice stays yours | ✕ | Partial3 | Partial3 | ✕ | |
| AI spend reported against delivered work | ✕ | ✕ | ✕ | ✕ | |
| A different job — and they’re welcome inside the loop | |||||
| In-IDE code generation for individual developers | ✕ | ||||
1 Specs stay inside one developer’s environment rather than becoming a shared, reviewable backlog.
2 Settings that apply to an individual’s use, not organisation-wide standards across a delivery team.
3 Limited choice within the vendor’s own catalogue or cloud.
Comparison reflects each product’s published capabilities as at August 2026 in its primary role; capabilities change quickly — verify against vendor documentation.
See your first loop close in 20 minutes.Bring one real requirement document and watch it become a governed backlog in your own board — with your team holding the sign-off.
Book a First Loop SessionThe questions delivery leaders, CTOs and procurement teams raise most often when they first see the loop.
Those models are engines inside OnTrack — and they're swappable, configured per customer, with your own provider keys if you prefer.
What you’re adopting is everything a raw model doesn’t have: role-specialised agents wired into Azure DevOps and Jira, human review gates, per-tenant guardrails, cost metering, and a traceability record running from the original requirement through to test evidence. The model is a fraction of that. Audit-grade test evidence is not something a chat window produces.
Keep it — genuinely. Copilot lives in the IDE and makes writing code faster, and nothing in OnTrack competes with that.
OnTrack covers the delivery effort that happens outside the IDE: getting from requirement documents to a groomed backlog, from stories to test suites, and from merges to evidence. If anything, a coding assistant strengthens the case — code now gets produced faster than most requirements and QA processes can keep up with. That’s the bottleneck we close.
You could build a convincing demo in a fortnight. What takes years is everything after the demo — the governance, isolation, integration depth and reliability that let a delivery organisation actually depend on it. That is several engineer-years of platform work with no relationship to your own product, and it is already ours.
That’s several engineer-years of platform work with no relationship to your actual product — and it is already ours.
Whichever one your policy allows. OnTrack is model-agnostic and configured per customer, with your own provider keys if you prefer them and encrypted credential storage. We support the major enterprise providers and can align to an approved-vendor list.
Prompts are versioned, governed assets on the platform rather than hardcoded bets on a single vendor. If your policy changes, your configuration changes; your delivery loop doesn’t.
Every customer is fully isolated from every other: your documents, your data and your credentials are kept separate and encrypted, and administrative actions on your account are recorded in an audit trail you can inspect. We'll walk your security team through the detail under NDA.
No. Agents propose; your people approve. Low-confidence output is held for human review by design rather than published straight to your backlog, so an analyst signs off before anything reaches the board.
The same discipline applies to quality: decisions that must stand up to audit are made in a way that is repeatable and explainable, not left to a model's judgement. “The AI decided it passed” is not an answer we would ever ask you to give.
No — the opposite. OnTrack publishes into the ALM your teams already use, and Azure DevOps and Jira are both supported today.
Your board stays your board. Stories, test cases and merge evidence simply arrive in it.
Start with one loop. Most customers begin with the BA agent on a single project’s documents, publishing into their existing board behind the human review gate — structured stories with acceptance criteria land in the backlog in the first session.
QA and evidence capture switch on when you’re ready. It’s plan- and seat-based, so scope follows your pace rather than a migration plan.
AI spend is reported against the delivery work it produced, broken down by project and by team. It means the budget conversation is about output rather than seat counts.
It means the AI budget conversation is about output rather than seat counts.
Still have a question?Bring it to a First Loop Session — twenty minutes, one of your real requirement documents, and your own board.
Book a First Loop Session
eBlocks Software helps enterprises transform software delivery so they can innovate faster, reduce delivery costs and improve business outcomes.
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